Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .

Metadata 78

Metadata Data Warehouse Snapshot Data Quality 78

DataKitchen

OCTOBER 11, 2021

Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. The automated orchestration published the data to an AWS S3 Data Lake. Monitoring Job Metadata. About the Author. Priyanjna Sharma.

Testing 152

Testing Metadata Dashboards Statistics 152

Alation

MARCH 29, 2021

In this blog, we will discuss how data catalogs accelerate search & discovery. How metadata accelerates search & discovery. A modern data catalog is more than just a collection of your enterprise’s every data asset. It shows not only who is using the data, but how. Context aids understanding. Conclusion.

Metadata 59

Metadata Data-driven Enterprise Machine Learning 59

Cloudera

MAY 19, 2021

Data Ingestion. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most data lakes exist as object stores full of parquet files. Parquet also stores type metadata which makes reading back and processing the files later slightly easier. P2 GPU instances are not supported.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

Cloudera

APRIL 3, 2023

The new feature this blog post is aiming to discuss about Iceberg V2 format (version 2), as the Iceberg table specification explains, the V1 format aimed to support large analytic data tables, while V2 aimed to add row level deletes and updates. Read why the future of data lakehouses is open.

Data Warehouse 81

Data Warehouse Snapshot Metadata Cost-Benefit 81

Cloudera

JUNE 4, 2018

The solution to this massive data challenge embedded the Aspire Content Processing Framework into the Cloudera Enterprise Data Hub as a Cloudera Parcel – a binary distribution format containing the program files, along with additional metadata used by Cloudera Manager. compliance reporting.

Unstructured Data 46

Unstructured Data Metadata Big Data Enterprise 46

In(tegrate) the Clouds

MAY 1, 2016

SnapLogic published Eight Data Management Requirements for the Enterprise Data Lake. They are: Storage and Data Formats. Metadata and Governance. The company also recently hosted a webinar on Democratizing the Data Lake with Constellation Research and published 2 whitepapers from Mark Madsen.

Data Lake 40

Data Lake Enterprise Management Metadata 40

erwin

AUGUST 31, 2023

It delivers the ability to capture and unify the business and technical perspectives of data assets, enables effective collaboration between a variety of stakeholders, and delivers metadata-driven automation to accelerate the creation and maintenance of data sources on virtually any data management platform. by Quest ®.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Andrew White

JANUARY 11, 2021

On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. The fill report is here: Leadership Vision for 2021: Data and Analytics. It really does. Great idea.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Alation

FEBRUARY 20, 2020

According to the report, “Demand for data catalogs is soaring as organizations struggle to inventory distributed data assets to facilitate data monetization and conform to regulations.” The tour stops at Gartner Symposium next month, where you can learn first hand why Gartner believes “Data Catalogs are the New Black.”.

Machine Learning 52

Machine Learning Metadata Reporting Data-driven 52

Alation

FEBRUARY 17, 2023

This is because you will know what data you can trust, and you will have processes to create and upkeep data, as well as curated metadata to exploit data’s full capabilities. Can you differentiate between governance of raw data and enhanced data (information)? Where do you govern? Here’s an example.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52